Load
Pulling code almost directly from LM1996-NumPoolComScaling-Results-2021-05.Rmd.
dirViking <- c(
file.path(
getwd(), "LCAB_LawMorton1996-NumericalPoolCommunityScaling"
),
file.path(
getwd(), "LCAB_LawMorton1996-NumericalPoolCommunityScaling2"
)
)
dirVikingResults <- file.path(
dirViking, c("results-2021-04", "save-2021-05-10") # Latter not 100% yet.
)
resultFormat <- paste0(
"run-",
"%d", # Combination Number, or CombnNum.
"-",
"%s", # Run Seed.
".RDS"
)
2021-04 Data
# Copied from LawMorton1996-NumericalPoolCommunityScaling-Calculation.R
set.seed(38427042)
basal <- c(3, 10, 30, 100, 300, 1000)
consumer <- c(3, 10, 30, 100, 300, 1000) * 2
events <- (max(basal) + max(consumer)) * 2
runs <- 100
logBodySize <- c(-2, -1, -1, 1) # Morton and Law 1997 version.
parameters <- c(0.01, 10, 0.5, 0.2, 100, 0.1)
# Need to rerun seedsPrep to get the random number generation right for seedsRun
seedsPrep <- runif(2 * length(basal) * length(consumer)) * 1E8
seedsRun <- runif(runs * length(basal) * length(consumer)) * 1E8
paramFrame <- with(list(
b = rep(basal, times = length(consumer)),
c = rep(consumer, each = length(basal)),
s1 = seedsPrep[1:(length(basal) * length(consumer))],
s2 = seedsPrep[
(length(basal) * length(consumer) + 1):(
2 * length(basal) * length(consumer))
],
sR = seedsRun
), {
temp <- data.frame(
CombnNum = 0,
Basals = b,
Consumers = c,
SeedPool = s1,
SeedMat = s2,
SeedRuns = "",
SeedRunsNum = 0,
EndStates = I(rep(list(""), length(b))),
EndStatesNum = 0,
EndStateSizes = I(rep(list(""), length(b))),
EndStateSizesNum = NA,
EndStateAssembly = I(rep(list(""), length(b))),
EndStateAbundance = I(rep(list(""), length(b))),
Dataset = "2021-04",
DatasetID = 1,
stringsAsFactors = FALSE
)
for (i in 1:nrow(temp)) {
seeds <- sR[((i - 1) * runs + 1) : (i * runs)]
temp$SeedRuns[i] <- toString(seeds) # CSV
temp$SeedRunsNum[i] <- length(seeds)
}
temp$CombnNum <- 1:nrow(temp)
temp
})
# Note: n + 2 end states. Failure to finish, failure to obtain state, and state.
for (i in 1:nrow(paramFrame)) {
resultsList <- list(
"No Run" = 0,
"No State" = 0
)
resultsSize <- list(
"0" = 0
)
resultsAssembly <- list(
"No Run" = data.frame(),
"No State" = data.frame()
)
seeds <- unlist(strsplit(paramFrame$SeedRuns[i], ', '))
for (seed in seeds) {
fileName <- file.path(
dirVikingResults[paramFrame$DatasetID[i]],
sprintf(resultFormat, paramFrame$CombnNum[i], seed)
)
if (file.exists(fileName)) {
temp <- load(fileName)
temp <- eval(parse(text = temp)) # Get objects.
if (is.data.frame(temp)) {
community <- toString(
temp[[ncol(temp)]][[nrow(temp)]]
)
size <- toString(length(temp[[ncol(temp)]][[nrow(temp)]]))
if (community == "") {
resultsList$`No State` <- resultsList$`No State` + 1
resultsSize$`0` <- resultsSize$`0` + 1
} else if (community %in% names(resultsList)) {
resultsList[[community]] <- resultsList[[community]] + 1
resultsSize[[size]] <- resultsSize[[size]] + 1
} else {
resultsList[[community]] <- 1
resultsAssembly[[community]] <- temp
if (size %in% resultsSize) {
resultsSize[[size]] <- resultsSize[[size]] + 1
} else {
resultsSize[[size]] <- 1
}
}
} else {
resultsList$`No State` <- resultsList$`No State` + 1
resultsSize$`0` <- resultsSize$`0` + 1
}
} else {
resultsList$`No Run` <- resultsList$`No Run` + 1
resultsSize$`0` <- resultsSize$`0` + 1
}
}
paramFrame$EndStates[[i]] <- resultsList
paramFrame$EndStatesNum[i] <- length(resultsList) - 2 # ! No State, No Run
paramFrame$EndStateSizes[[i]] <- resultsSize
paramFrame$EndStateSizesNum[i] <- length(resultsSize) - 1 # ! 0
paramFrame$EndStateAssembly[[i]] <- resultsAssembly
}
2021-05 Data
source(
file.path(getwd(),
"LawMorton1996-NumericalPoolCommunityScaling-Settings2.R")
)
oldNrow <- nrow(paramFrame)
paramFrame <- rbind(paramFrame, with(list(
b = rep(basal, times = length(consumer)),
c = rep(consumer, each = length(basal)),
s1 = seedsPrep[1:(length(basal) * length(consumer))],
s2 = seedsPrep[
(length(basal) * length(consumer) + 1):(
2 * length(basal) * length(consumer))
],
sR = seedsRun
), {
temp <- data.frame(
CombnNum = 0,
Basals = b,
Consumers = c,
SeedPool = s1,
SeedMat = s2,
SeedRuns = "",
SeedRunsNum = 0,
EndStates = I(rep(list(""), length(b))),
EndStatesNum = 0,
EndStateSizes = I(rep(list(""), length(b))),
EndStateSizesNum = NA,
EndStateAssembly = I(rep(list(""), length(b))),
EndStateAbundance = I(rep(list(""), length(b))),
Dataset = "2021-05",
DatasetID = 2,
stringsAsFactors = FALSE
)
for (i in 1:nrow(temp)) {
seeds <- sR[((i - 1) * runs + 1) : (i * runs)]
temp$SeedRuns[i] <- toString(seeds) # CSV
temp$SeedRunsNum[i] <- length(seeds)
}
temp$CombnNum <- 1:nrow(temp)
temp
})
)
# Note: n + 2 end states. Failure to finish, failure to obtain state, and state.
# Modified from above, but with the abundance recorded.
for (i in (oldNrow + 1):nrow(paramFrame)) {
resultsList <- list(
"No Run" = 0,
"No State" = 0
)
resultsSize <- list(
"0" = 0
)
resultsAssembly <- list(
"No Run" = data.frame(),
"No State" = data.frame()
)
resultsAbund <- list(
"No Run" = "",
"No State" = ""
)
seeds <- unlist(strsplit(paramFrame$SeedRuns[i], ', '))
for (seed in seeds) {
fileName <- file.path(
dirVikingResults[paramFrame$DatasetID[i]],
sprintf(resultFormat, paramFrame$CombnNum[i], seed)
)
if (file.exists(fileName)) {
temp <- load(fileName)
temp <- eval(parse(text = temp)) # Get objects.
if (is.list(temp) && "Result" %in% names(temp)) {
if (is.data.frame(temp$Result))
community <- temp$Result$Community[[nrow(temp$Result)]]
else
community <- temp$Result
size <- toString(length(community))
if (community[1] != "")
abund <- toString(temp$Abund[community + 1])
else
abund <- ""
community <- toString(community)
if (community == "") {
resultsList$`No State` <- resultsList$`No State` + 1
resultsSize$`0` <- resultsSize$`0` + 1
} else if (community %in% names(resultsList)) {
resultsList[[community]] <- resultsList[[community]] + 1
resultsSize[[size]] <- resultsSize[[size]] + 1
} else {
resultsList[[community]] <- 1
resultsAssembly[[community]] <- temp
resultsAbund[[community]] <- abund
if (size %in% resultsSize) {
resultsSize[[size]] <- resultsSize[[size]] + 1
} else {
resultsSize[[size]] <- 1
}
}
} else {
resultsList$`No State` <- resultsList$`No State` + 1
resultsSize$`0` <- resultsSize$`0` + 1
}
} else {
resultsList$`No Run` <- resultsList$`No Run` + 1
resultsSize$`0` <- resultsSize$`0` + 1
}
}
paramFrame$EndStates[[i]] <- resultsList
paramFrame$EndStatesNum[i] <- length(resultsList) - 2 # ! No State, No Run
paramFrame$EndStateSizes[[i]] <- resultsSize
paramFrame$EndStateSizesNum[i] <- length(resultsSize) - 1 # ! 0
paramFrame$EndStateAssembly[[i]] <- resultsAssembly
paramFrame$EndStateAbundance[[i]] <- resultsAbund
}
Test Data
testRowNums <- nrow(paramFrame)
testRowsToAdd <- c(2, 6) # Make sure to put in numerical order!
paramFrame <- with(
list(
basal2 = c(5, 10, 15),
consumer2 = c(20, 40, 60),
logBodySize = c(-2, -1, -1, 0),
parameters = c(0.01, 10, 0.5, 0.2, 100, 0.1)
),
{
set.seed(3680180)
seedsPrep2 <- runif(2 * length(basal2) * length(consumer2)) * 1E8
with(list(
b = rep(basal2, times = length(consumer2)),
c = rep(consumer2, each = length(basal2)),
s1 = seedsPrep2[1:(length(basal2) * length(consumer2))],
s2 = seedsPrep2[
(length(basal2) * length(consumer2) + 1):(
2 * length(basal2) * length(consumer2))
]
), {
rbind(
paramFrame,
data.frame(
CombnNum = testRowsToAdd,
Basals = b[testRowsToAdd],
Consumers = c[testRowsToAdd],
SeedPool = s1[testRowsToAdd],
SeedMat = s2[testRowsToAdd],
SeedRuns = "",
SeedRunsNum = 0,
EndStates = I(rep(list(""), length(testRowsToAdd))),
EndStatesNum = 0,
EndStateSizes = I(rep(list(""), length(testRowsToAdd))),
EndStateSizesNum = NA,
EndStateAssembly = I(rep(list(""), length(testRowsToAdd))),
EndStateAbundance = I(rep(list(""), length(testRowsToAdd))),
Dataset = "Test",
DatasetID = max(paramFrame$DatasetID) + 1,
stringsAsFactors = FALSE
)
)
}
)
}
)
testRowNums <- (testRowNums + 1):nrow(paramFrame)
resultsList <- list(
list(
"No Run" = 0,
"No State" = 0,
"2, 4, 6, 12, 29" = 1,
"2, 4, 6, 13, 29" = 1
),
list(
"No Run" = 0,
"No State" = 0,
"8, 10, 12, 14, 15, 16, 39, 43" = 1,
"8, 12, 14, 15, 16, 38, 39" = 1
)
)
resultsSize <- list(
list(
"0" = 0,
"5" = 2
),
list(
"0" = 0,
"8" = 1,
"7" = 1
)
)
resultsAbund <- list(
list(
"No Run" = "",
"No State" = "",
"2, 4, 6, 12, 29" = "742.88553671712, 80.579233072626, 162.128399850253, 20.2082198699389, 18.8589490510429",
"2, 4, 6, 13, 29" = "668.664143581837, 119.024146851052, 127.680269383867, 30.657960866033, 13.4844194707944"
),
list(
"No Run" = "",
"No State" = "",
"8, 10, 12, 14, 15, 16, 39, 43" = "20.7665807606491, 32.4461165261454, 80.4033387818895, 817.879722033354, 121.136570782828, 18.0390671088957, 12.3834561177271, 19.9674718543196",
"8, 12, 14, 15, 16, 38, 39" = "82.592048492812, 138.267379166014, 938.158436379166, 51.8610963745021, 5.03556251837491, 14.1019343145825, 25.9231062711228"
)
)
for (j in seq_along(testRowNums)) {
i <- testRowNums[j]
paramFrame$EndStates[[i]] <- resultsList[[j]]
paramFrame$EndStatesNum[i] <- length(resultsList[[j]]) - 2 # ! No State, No Run
paramFrame$EndStateSizes[[i]] <- resultsSize[[j]]
paramFrame$EndStateSizesNum[i] <- length(resultsSize[[j]]) - 1 # ! 0
paramFrame$EndStateAbundance[[i]] <- resultsAbund[[j]]
}
Plot
# X, Y, Basal and Consumer.
# Z = Sizes of the Endstates.
plotScalingData <- data.frame(
CombnNum = rep(paramFrame$CombnNum, paramFrame$EndStatesNum),
Basals = rep(paramFrame$Basals, paramFrame$EndStatesNum),
Consumers = rep(paramFrame$Consumers, paramFrame$EndStatesNum),
Dataset = rep(paramFrame$Dataset, paramFrame$EndStatesNum),
DatasetID = rep(paramFrame$DatasetID, paramFrame$EndStatesNum)
)
# Communities
comms <- unlist(lapply(paramFrame$EndStates, names))
freqs <- unlist(paramFrame$EndStates)
asmbl <- unlist(paramFrame$EndStateAssembly, recursive = FALSE)
asmbl <- asmbl[comms != "No Run" & comms != "No State"]
freqs <- freqs[comms != "No Run" & comms != "No State"]
comms <- comms[comms != "No Run" & comms != "No State"]
asmbl <- lapply(asmbl, function(d) {
if (is.null(d)) return(NA)
if ("Result.Outcome" %in% names(d))
d %>% dplyr::filter(Result.Outcome != "Type 1 (Failure)" &
Result.Outcome != "Present")
else
d$Result %>% dplyr::filter(Outcome != "Type 1 (Failure)" &
Outcome != "Present")
})
plotScalingData$Communities <- comms
plotScalingData$CommunityFreq <- freqs
plotScalingData$CommunitySeq <- asmbl
# Community Size
temp <- unlist(lapply(strsplit(plotScalingData$Communities, ','), length))
plotScalingData$CommunitySize <- temp
# For usage by the reader.
plotScaling <- plotly::plot_ly(
plotScalingData,
x = ~Basals,
y = ~Consumers,
z = ~CommunitySize,
color = ~Dataset,
colors = c("red", "blue", "black")
)
plotScaling <- plotly::add_markers(plotScaling)
plotScaling <- plotly::layout(
plotScaling,
scene = list(
xaxis = list(type = "log"),
yaxis = list(type = "log"),
camera = list(
eye = list(
x = -1.25, y = -1.25, z = .05
)
)
)
)
plotScaling
Abundances
# > runif(1) * 1E8
# [1] 82598679
set.seed(82598679)
mats <- list()
poolsall <- list() # name pools used in save data; be careful!
for (i in 1:length(dirViking)) {
temp <- load(file.path(
dirViking[i],
paste0("LawMorton1996-NumericalPoolCommunityScaling-PoolMats",
if (i > 1) i else "",
".RDS")
))
mats[[i]] <- eval(parse(text = temp[1]))
poolsall[[i]] <- eval(parse(text = temp[2]))
}
pools <- poolsall
# Add in the test datasets.
poolsTemp <- list()
matsTemp <- list()
for (r in testRowNums) {
testRowRow <- paramFrame[r, ]
poolsTemp[[testRowRow$CombnNum]] <- with(testRowRow,
RMTRCode2::LawMorton1996_species(
Basal = Basals,
Consumer = Consumers,
Parameters = c(0.01, 10, 0.5, 0.2, 100, 0.1),
LogBodySize = c(-2, -1, -1, 0),
seed = SeedPool
)
)
matsTemp[[testRowRow$CombnNum]] <- with(testRowRow,
RMTRCode2::LawMorton1996_CommunityMat(
Pool = poolsTemp[[CombnNum]],
Parameters = c(0.01, 10, 0.5, 0.2, 100, 0.1),
seed = SeedMat
)
)
}
executing %dopar% sequentially: no parallel backend registered
pools[[i + 1]] <- poolsTemp
mats[[i + 1]] <- matsTemp
oldCandidateData <- load(file.path(getwd(), "candidateDataSoFar.Rdata"))
oldCandidateData <- eval(parse(text = oldCandidateData))
candidateData <- plotScalingData %>% dplyr::group_by(
CombnNum, Dataset
) %>% dplyr::mutate(
OtherSteadyStates = dplyr::n() - 1
) %>% dplyr::filter(
OtherSteadyStates > 0
)
candidateData %>% dplyr::select(-CommunitySeq)
# First, check if it is in the paramFrame.
# Second, check if it is in the saved data from the previous.
# Otherwise, ignore it, we'll figure out what it is and why it is missing later.
candidateData$CommunityAbund <- ""
for (r in 1:nrow(candidateData)) {
# ID 1:4 are used to identify paramFrame, 5 used to identify abundance
ID <- candidateData[r, 1:6]
paramFrameRow <- paramFrame %>% dplyr::filter(
CombnNum == ID$CombnNum,
Basals == ID$Basals,
Consumers == ID$Consumers,
Dataset == ID$Dataset
)
if (is.list(paramFrameRow$EndStateAbundance[[1]])) {
entry <- which(ID$Communities == names(paramFrameRow$EndStateAbundance[[1]]))
if (length(entry)) {
candidateData$CommunityAbund[r] <- paramFrameRow$EndStateAbundance[[1]][[entry]]
next()
}
}
if (ID$Dataset == "2021-04") {
oldCandDatRow <- oldCandidateData %>% dplyr::filter(
CombnNum == ID$CombnNum,
Basals == ID$Basals,
Consumers == ID$Consumers,
Communities == ID$Communities
)
if (nrow(oldCandDatRow) > 0) {
if (oldCandDatRow$CommunityAbund != "") {
candidateData$CommunityAbund[r] <- oldCandDatRow$CommunityAbund
}
}
}
}
for (r in 1:nrow(candidateData)) {
if (!(candidateData$CommunityAbund[r] == "Failure" |
candidateData$CommunityAbund[r] == "")) next
# Random guesses, starting from structured.
temp <- with(
candidateData[r, ],
RMTRCode2::FindSteadyStateFromEstimate(
Pool = pools[[DatasetID]][[CombnNum]],
InteractionMatrix = mats[[DatasetID]][[CombnNum]],
Community = Communities,
Populations = ifelse(
pools[[DatasetID]][[CombnNum]]$Type[
RMTRCode2::CsvRowSplit(Communities)
] == "Basal",
1000, 10)
)
)
if (any(temp) < 1E-4) {
temp <- "EstimateFailure"
} else {
temp <- toString(temp)
}
candidateData$CommunityAbund[r] <- temp
}
DLSODA- At current T (=R1), MXSTEP (=I1) steps
taken on this call before reaching TOUT
In above message, I1 = 5000
In above message, R1 = 2843.06
an excessive amount of work (> maxsteps ) was done, but integration was not successful - increase maxstepsReturning early. Results are accurate, as far as they go
DLSODA- At current T (=R1), MXSTEP (=I1) steps
taken on this call before reaching TOUT
In above message, I1 = 5000
In above message, R1 = 2876.33
an excessive amount of work (> maxsteps ) was done, but integration was not successful - increase maxstepsReturning early. Results are accurate, as far as they go
DLSODA- At current T (=R1), MXSTEP (=I1) steps
taken on this call before reaching TOUT
In above message, I1 = 5000
In above message, R1 = 2870.65
an excessive amount of work (> maxsteps ) was done, but integration was not successful - increase maxstepsReturning early. Results are accurate, as far as they go
DLSODA- At current T (=R1), MXSTEP (=I1) steps
taken on this call before reaching TOUT
In above message, I1 = 5000
In above message, R1 = 2873.23
an excessive amount of work (> maxsteps ) was done, but integration was not successful - increase maxstepsReturning early. Results are accurate, as far as they go
DLSODA- At current T (=R1), MXSTEP (=I1) steps
taken on this call before reaching TOUT
In above message, I1 = 5000
In above message, R1 = 2871.69
an excessive amount of work (> maxsteps ) was done, but integration was not successful - increase maxstepsReturning early. Results are accurate, as far as they gocoercing argument of type 'double' to logicalcoercing argument of type 'double' to logicalcoercing argument of type 'double' to logicalcoercing argument of type 'double' to logicalcoercing argument of type 'double' to logicalcoercing argument of type 'double' to logical
DLSODA- At current T (=R1), MXSTEP (=I1) steps
taken on this call before reaching TOUT
In above message, I1 = 5000
In above message, R1 = 6597.38
an excessive amount of work (> maxsteps ) was done, but integration was not successful - increase maxstepsReturning early. Results are accurate, as far as they gocoercing argument of type 'double' to logical
DLSODA- At current T (=R1), MXSTEP (=I1) steps
taken on this call before reaching TOUT
In above message, I1 = 5000
In above message, R1 = 3592.76
an excessive amount of work (> maxsteps ) was done, but integration was not successful - increase maxstepsReturning early. Results are accurate, as far as they go
DLSODA- At current T (=R1), MXSTEP (=I1) steps
taken on this call before reaching TOUT
In above message, I1 = 5000
In above message, R1 = 3780.53
an excessive amount of work (> maxsteps ) was done, but integration was not successful - increase maxstepsReturning early. Results are accurate, as far as they go
DLSODA- At current T (=R1), MXSTEP (=I1) steps
taken on this call before reaching TOUT
In above message, I1 = 5000
In above message, R1 = 3786.89
an excessive amount of work (> maxsteps ) was done, but integration was not successful - increase maxstepsReturning early. Results are accurate, as far as they go
DLSODA- At current T (=R1), MXSTEP (=I1) steps
taken on this call before reaching TOUT
In above message, I1 = 5000
In above message, R1 = 3784.07
an excessive amount of work (> maxsteps ) was done, but integration was not successful - increase maxstepsReturning early. Results are accurate, as far as they go
DLSODA- At current T (=R1), MXSTEP (=I1) steps
taken on this call before reaching TOUT
In above message, I1 = 5000
In above message, R1 = 3778.43
an excessive amount of work (> maxsteps ) was done, but integration was not successful - increase maxstepsReturning early. Results are accurate, as far as they go
DLSODA- At current T (=R1), MXSTEP (=I1) steps
taken on this call before reaching TOUT
In above message, I1 = 5000
In above message, R1 = 3777.75
an excessive amount of work (> maxsteps ) was done, but integration was not successful - increase maxstepsReturning early. Results are accurate, as far as they go
DLSODA- At current T (=R1), MXSTEP (=I1) steps
taken on this call before reaching TOUT
In above message, I1 = 5000
In above message, R1 = 3774.98
an excessive amount of work (> maxsteps ) was done, but integration was not successful - increase maxstepsReturning early. Results are accurate, as far as they go
DLSODA- At current T (=R1), MXSTEP (=I1) steps
taken on this call before reaching TOUT
In above message, I1 = 5000
In above message, R1 = 3781.84
an excessive amount of work (> maxsteps ) was done, but integration was not successful - increase maxstepsReturning early. Results are accurate, as far as they gocoercing argument of type 'double' to logical
candidateData <- candidateData %>% dplyr::filter(CommunityAbund != "",
CommunityAbund != "Failure",
CommunityAbund != "EstimateFailure")
candidateData$CommunityProd <- NA
for (r in 1:nrow(candidateData)) {
candidateData$CommunityProd[r] <- with(
candidateData[r, ],
RMTRCode2::Productivity(
Pool = pools[[DatasetID]][[CombnNum]],
InteractionMatrix = mats[[DatasetID]][[CombnNum]],
Community = Communities,
Populations = CommunityAbund
)
)
}
Simple Island Results
islandFUN <- function(i, dat, pool, mat, dmat) {
temp <- dat[i, ]
RMTRCode2::IslandDynamics(
Pool = pool,
InteractionMatrix = mat,
Communities = c(
list(temp$Communities[1]),
rep("", nrow(dmat) - 2),
temp$Communities[2]
),
Populations = c(
list(temp$CommunityAbund[1]),
rep("", nrow(dmat) - 2),
list(temp$CommunityAbund[2])
),
DispersalPool = 0.0001,
DispersalIsland = dmat,
Verbose = FALSE
)
}
# For each group-dataset,
# For each pair,
# Run Island Dynamics,
# Save the result with its pairing
candidateData$TotalID <- paste(candidateData$CombnNum, candidateData$DatasetID)
islandInteractionsOneTwo <- list()
for (grp in unique(candidateData$TotalID)) {
candidateDataSubset <- candidateData %>% dplyr::filter(TotalID == grp)
if (nrow(candidateDataSubset) == 1) next()
pairingResults <- combn(
nrow(candidateDataSubset), 2,
islandFUN,
dat = candidateDataSubset,
pool = pools[[
candidateDataSubset$DatasetID[1]
]][[candidateDataSubset$CombnNum[1]]],
mat = mats[[
candidateDataSubset$DatasetID[1]
]][[candidateDataSubset$CombnNum[1]]],
dmat = matrix(c(0, 1, 1, 0), nrow = 2, ncol = 2),
simplify = FALSE
)
pairingResults <- lapply(
pairingResults, function(mat, isles) {
mat <- mat[nrow(mat), -1]
retVal <- list()
species <- length(mat) / isles
for (i in 1:isles) {
retVal[[i]] <- mat[((i - 1) * species + 1) : (i * species)]
}
retVal
},
isles = 2
)
islandInteractionsOneTwo[[grp]] <- pairingResults
}
islandInteractionsOneEmptyTwo <- list()
for (grp in unique(candidateData$TotalID)) {
candidateDataSubset <- candidateData %>% dplyr::filter(TotalID == grp)
if (nrow(candidateDataSubset) == 1) next()
pairingResults <- combn(
nrow(candidateDataSubset), 2,
islandFUN,
dat = candidateDataSubset,
pool = pools[[
candidateDataSubset$DatasetID[1]
]][[candidateDataSubset$CombnNum[1]]],
mat = mats[[
candidateDataSubset$DatasetID[1]
]][[candidateDataSubset$CombnNum[1]]],
dmat = matrix(c(
0, 1, 0, # Island 2 -> 1
1, 0, 1, # Island 1 -> 2, Island 3 -> 2
0, 1, 0 # Island 2 -> 3
), nrow = 3, ncol = 3, byrow = TRUE),
simplify = FALSE
)
pairingResults <- lapply(
pairingResults, function(mat, isles) {
mat <- mat[nrow(mat), -1]
retVal <- list()
species <- length(mat) / isles
for (i in 1:isles) {
retVal[[i]] <- mat[((i - 1) * species + 1) : (i * species)]
}
retVal
},
isles = 3
)
islandInteractionsOneEmptyTwo[[grp]] <- pairingResults
}
# Format of table should be:
# ID, Community 1, Community 2, Outcomes 1-2, Outcomes 1-0-2
# For outcomes, species presence will be used.
# communities <- NULL
# totalCommunities <- NULL
# for (grp in unique(candidateData$TotalID)) {
# candidateDataSubset <- candidateData %>% dplyr::filter(TotalID == grp)
#
# if (nrow(candidateDataSubset) > 1) {
# newCommunities <- combn(
# candidateDataSubset$Communities, 2,
# )
# communities <- c(communities, newCommunities)
# totalCommunities <- c(
# totalCommunities,
# toString(sort(unique(unlist(lapply(newCommunities,
# RMTRCode2::CsvRowSplit)))))
# )
# }
# }
communities <- NULL
totalCommunities <- NULL
for (grp in unique(candidateData$TotalID)) {
candidateDataSubset <- candidateData %>% dplyr::filter(TotalID == grp)
if (nrow(candidateDataSubset) > 1) {
newCommunities <- combn(
candidateDataSubset$Communities, 2,
)
communities <- c(communities, newCommunities)
totalCommunities <- c( # Labelling is wrong here. Need tC from pairs of nC.
totalCommunities,
list(apply(newCommunities, 2, function(coms) {
toString(sort(unique(RMTRCode2::CsvRowSplit(coms))))
}))
)
}
}
# Make sure that the entries match in terms of lengths.
stopifnot(
isTRUE(all.equal(
lapply(islandInteractionsOneEmptyTwo, length),
lapply(totalCommunities, length),
check.names = FALSE
)),
isTRUE(all.equal(
lapply(islandInteractionsOneEmptyTwo, function(d) {lapply(d, function(v) length(v[[1]]))}),
lapply(totalCommunities, function(d) {lapply(d, function(s) length(RMTRCode2::CsvRowSplit(s)))}),
check.names = FALSE
))
)
minThresh <- 1E-6
islandInteractionsOneTwoWhich <- unlist(lapply(
seq_along(islandInteractionsOneTwo), function(i, byID, communities) {
lapply(seq_along(byID[[i]]), function(j, system, comms) {
lapply(system[[j]], function(pipeEnd, comms) {
toString(RMTRCode2::CsvRowSplit(comms)[
which(pipeEnd > minThresh)
])
}, comms = comms[[j]])
}, system = byID[[i]], comms = communities[[i]])
}, byID = islandInteractionsOneTwo, communities = totalCommunities
))
islandInteractionsOneEmptyTwoWhich <- unlist(lapply(
seq_along(islandInteractionsOneEmptyTwo), function(i, byID, communities) {
lapply(seq_along(byID[[i]]), function(j, system, comms) {
lapply(system[[j]], function(pipeEnd, comms) {
toString(RMTRCode2::CsvRowSplit(comms)[
which(pipeEnd > minThresh)
])
}, comms = comms[[j]])
}, system = byID[[i]], comms = communities[[i]])
}, byID = islandInteractionsOneEmptyTwo, communities = totalCommunities
))
# islandInteractionsOneTwoWhich <- unlist(lapply(
# seq_along(islandInteractionsOneTwo), function(i, x, tC) {
# lapply(x[[i]], function(y, tC) {
# lapply(y, function(z, tC) {
# toString(RMTRCode2::CsvRowSplit(tC)[which(z > 1E-6)])
# }, tC = tC)
# },
# tC = tC[i])
# },
# x = islandInteractionsOneTwo,
# tC = totalCommunities))
#
# islandInteractionsOneEmptyTwoWhich <- unlist(lapply(
# seq_along(islandInteractionsOneEmptyTwo), function(i, x, tC) {
# lapply(x[[i]], function(y, tC) {
# lapply(y, function(z, tC) {
# toString(RMTRCode2::CsvRowSplit(tC)[which(z > 1E-6)])
# }, tC = tC)
# },
# tC = tC[i])
# },
# x = islandInteractionsOneEmptyTwo,
# tC = totalCommunities))
islandInteractionResults <- data.frame(
DatasetID = rep(names(islandInteractionsOneTwo),
unlist(lapply(islandInteractionsOneTwo, length))),
Community1 = communities[seq(from = 1, to = length(communities), by = 2)],
Community2 = communities[seq(from = 2, to = length(communities), by = 2)],
OutcomeWOEmpty_Island1 = islandInteractionsOneTwoWhich[
seq(from = 1, to = length(islandInteractionsOneTwoWhich), by = 2)],
OutcomeWOEmpty_Island2 = islandInteractionsOneTwoWhich[
seq(from = 1, to = length(islandInteractionsOneTwoWhich), by = 2)],
OutcomeWEmpty_Island1 = islandInteractionsOneEmptyTwoWhich[
seq(from = 1, to = length(islandInteractionsOneEmptyTwoWhich), by = 3)],
OutcomeWEmpty_Island2 = islandInteractionsOneEmptyTwoWhich[
seq(from = 1, to = length(islandInteractionsOneEmptyTwoWhich), by = 3)],
OutcomeWEmpty_Island3 = islandInteractionsOneEmptyTwoWhich[
seq(from = 1, to = length(islandInteractionsOneEmptyTwoWhich), by = 3)]
)
islandInteractionResults
islandInteractionResults %>% dplyr::mutate(
C1WOInvaded = Community1 != OutcomeWOEmpty_Island1,
C2WOInvaded = Community2 != OutcomeWOEmpty_Island2,
C1WInvaded = Community1 != OutcomeWEmpty_Island1,
C2WInvaded = Community2 != OutcomeWEmpty_Island3,
StalemateWO = !C1WOInvaded & !C2WOInvaded,
StalemateW = !C1WInvaded & !C2WInvaded,
HybridWO = C1WOInvaded & C2WOInvaded,
HybridW = C1WInvaded & C2WInvaded,
) %>% dplyr::select(-dplyr::starts_with("Outcome"))
Outcomes after fix. Note the differences in the last column for easiest comparison. –>
islandInteractionResults %>% dplyr::mutate(
C1WOInvaded = Community1 != OutcomeWOEmpty_Island1,
C2WOInvaded = Community2 != OutcomeWOEmpty_Island2,
C1WInvaded = Community1 != OutcomeWEmpty_Island1,
C2WInvaded = Community2 != OutcomeWEmpty_Island3,
StalemateWO = !C1WOInvaded & !C2WOInvaded,
StalemateW = !C1WInvaded & !C2WInvaded,
HybridWO = C1WOInvaded & C2WOInvaded,
HybridW = C1WInvaded & C2WInvaded,
) %>% dplyr::select(-dplyr::starts_with("Outcome"))
---
title: "Answering Questions; Gather Data, 2021-05"
output:
  html_notebook:
    code_folding: hide
---

```{r libs}
# Check requisite packages are installed.
packages <- c(
  "plotly", 
  "dplyr"
)
for (pkg in packages) {
  library(pkg, character.only = TRUE)
}
```

# Load
Pulling code almost directly from `LM1996-NumPoolComScaling-Results-2021-05.Rmd`.
```{r dirs}
dirViking <- c(
  file.path(
    getwd(), "LCAB_LawMorton1996-NumericalPoolCommunityScaling"
  ),
  file.path(
    getwd(), "LCAB_LawMorton1996-NumericalPoolCommunityScaling2"
  )
)
dirVikingResults <- file.path(
  dirViking, c("results-2021-04", "save-2021-05-10") # Latter not 100% yet.
)
resultFormat <- paste0(
  "run-", 
  "%d", # Combination Number, or CombnNum.
  "-", 
  "%s", # Run Seed.
  ".RDS"
)
```

## 2021-04 Data
```{r params}
# Copied from LawMorton1996-NumericalPoolCommunityScaling-Calculation.R
set.seed(38427042)

basal <- c(3, 10, 30, 100, 300, 1000)
consumer <- c(3, 10, 30, 100, 300, 1000) * 2
events <- (max(basal) + max(consumer)) * 2
runs <- 100

logBodySize <- c(-2, -1, -1, 1) # Morton and Law 1997 version.
parameters <- c(0.01, 10, 0.5, 0.2, 100, 0.1)

# Need to rerun seedsPrep to get the random number generation right for seedsRun
seedsPrep <- runif(2 * length(basal) * length(consumer)) * 1E8
seedsRun <- runif(runs * length(basal) * length(consumer)) * 1E8
```

```{r organiseParams}
paramFrame <- with(list(
  b = rep(basal, times = length(consumer)),
  c = rep(consumer, each = length(basal)),
  s1 = seedsPrep[1:(length(basal) * length(consumer))],
  s2 = seedsPrep[
    (length(basal) * length(consumer) + 1):(
      2 * length(basal) * length(consumer))
  ],
  sR = seedsRun
), {
  temp <- data.frame(
    CombnNum = 0,
    Basals = b,
    Consumers = c,
    SeedPool = s1,
    SeedMat = s2,
    SeedRuns = "",
    SeedRunsNum = 0,
    EndStates = I(rep(list(""), length(b))),
    EndStatesNum = 0,
    EndStateSizes = I(rep(list(""), length(b))),
    EndStateSizesNum = NA,
    EndStateAssembly = I(rep(list(""), length(b))),
    EndStateAbundance = I(rep(list(""), length(b))),
    Dataset = "2021-04",
    DatasetID = 1,
    stringsAsFactors = FALSE
  )
  for (i in 1:nrow(temp)) {
    seeds <- sR[((i - 1) * runs + 1) : (i * runs)]
    temp$SeedRuns[i] <- toString(seeds) # CSV
    temp$SeedRunsNum[i] <- length(seeds)
  }
  temp$CombnNum <- 1:nrow(temp)
  temp
})
```

```{r loadResults}
# Note: n + 2 end states. Failure to finish, failure to obtain state, and state.
for (i in 1:nrow(paramFrame)) {
  resultsList <- list(
    "No Run" = 0,
    "No State" = 0
  )
  resultsSize <- list(
    "0" = 0
  )
  resultsAssembly <- list(
    "No Run" = data.frame(),
    "No State" = data.frame()
  )
  seeds <- unlist(strsplit(paramFrame$SeedRuns[i], ', '))
  for (seed in seeds) {
    fileName <- file.path(
      dirVikingResults[paramFrame$DatasetID[i]],
      sprintf(resultFormat, paramFrame$CombnNum[i], seed)
    )
    
    if (file.exists(fileName)) {
      temp <- load(fileName)
      temp <- eval(parse(text = temp)) # Get objects.
      
      if (is.data.frame(temp)) {
        community <- toString(
          temp[[ncol(temp)]][[nrow(temp)]]
        )
        size <- toString(length(temp[[ncol(temp)]][[nrow(temp)]]))
        
        if (community == "") {
          resultsList$`No State` <- resultsList$`No State` + 1
          resultsSize$`0` <- resultsSize$`0` + 1
          
        } else if (community %in% names(resultsList)) {
          resultsList[[community]] <- resultsList[[community]] + 1
          resultsSize[[size]] <- resultsSize[[size]] + 1
          
        } else {
          resultsList[[community]] <- 1
          resultsAssembly[[community]] <- temp
          
          if (size %in% resultsSize) {
            resultsSize[[size]] <- resultsSize[[size]] + 1
          } else {
            resultsSize[[size]] <- 1
          }
        }
      } else {
        resultsList$`No State` <- resultsList$`No State` + 1
        resultsSize$`0` <- resultsSize$`0` + 1
      }
    } else {
      resultsList$`No Run` <- resultsList$`No Run` + 1
      resultsSize$`0` <- resultsSize$`0` + 1
    }
  }
  
  paramFrame$EndStates[[i]] <- resultsList
  paramFrame$EndStatesNum[i] <- length(resultsList) - 2 # ! No State, No Run
  paramFrame$EndStateSizes[[i]] <- resultsSize
  paramFrame$EndStateSizesNum[i] <- length(resultsSize) - 1 # ! 0
  paramFrame$EndStateAssembly[[i]] <- resultsAssembly
}
```

## 2021-05 Data
```{r organiseParams2}
source(
  file.path(getwd(), 
            "LawMorton1996-NumericalPoolCommunityScaling-Settings2.R")
)

oldNrow <- nrow(paramFrame)

paramFrame <- rbind(paramFrame, with(list(
  b = rep(basal, times = length(consumer)),
  c = rep(consumer, each = length(basal)),
  s1 = seedsPrep[1:(length(basal) * length(consumer))],
  s2 = seedsPrep[
    (length(basal) * length(consumer) + 1):(
      2 * length(basal) * length(consumer))
  ],
  sR = seedsRun
), {
  temp <- data.frame(
    CombnNum = 0,
    Basals = b,
    Consumers = c,
    SeedPool = s1,
    SeedMat = s2,
    SeedRuns = "",
    SeedRunsNum = 0,
    EndStates = I(rep(list(""), length(b))),
    EndStatesNum = 0,
    EndStateSizes = I(rep(list(""), length(b))),
    EndStateSizesNum = NA,
    EndStateAssembly = I(rep(list(""), length(b))),
    EndStateAbundance = I(rep(list(""), length(b))),
    Dataset = "2021-05",
    DatasetID = 2,
    stringsAsFactors = FALSE
  )
  for (i in 1:nrow(temp)) {
    seeds <- sR[((i - 1) * runs + 1) : (i * runs)]
    temp$SeedRuns[i] <- toString(seeds) # CSV
    temp$SeedRunsNum[i] <- length(seeds)
  }
  temp$CombnNum <- 1:nrow(temp)
  temp
})
)
```

```{r loadResults2}
# Note: n + 2 end states. Failure to finish, failure to obtain state, and state.
# Modified from above, but with the abundance recorded.
for (i in (oldNrow + 1):nrow(paramFrame)) {
  resultsList <- list(
    "No Run" = 0,
    "No State" = 0
  )
  resultsSize <- list(
    "0" = 0
  )
  resultsAssembly <- list(
    "No Run" = data.frame(),
    "No State" = data.frame()
  )
  resultsAbund <- list(
    "No Run" = "",
    "No State" = ""
  )
  seeds <- unlist(strsplit(paramFrame$SeedRuns[i], ', '))
  for (seed in seeds) {
    fileName <- file.path(
      dirVikingResults[paramFrame$DatasetID[i]],
      sprintf(resultFormat, paramFrame$CombnNum[i], seed)
    )
    
    if (file.exists(fileName)) {
      temp <- load(fileName)
      temp <- eval(parse(text = temp)) # Get objects.
      
      if (is.list(temp) && "Result" %in% names(temp)) {
        
        if (is.data.frame(temp$Result))
          community <- temp$Result$Community[[nrow(temp$Result)]]
        else 
          community <- temp$Result
        
        size <- toString(length(community))
        
        if (community[1] != "") 
          abund <- toString(temp$Abund[community + 1])
        else 
          abund <- ""
        
        community <- toString(community)
        
        if (community == "") {
          resultsList$`No State` <- resultsList$`No State` + 1
          resultsSize$`0` <- resultsSize$`0` + 1
          
        } else if (community %in% names(resultsList)) {
          resultsList[[community]] <- resultsList[[community]] + 1
          resultsSize[[size]] <- resultsSize[[size]] + 1
          
        } else {
          resultsList[[community]] <- 1
          resultsAssembly[[community]] <- temp
          resultsAbund[[community]] <- abund
          
          if (size %in% resultsSize) {
            resultsSize[[size]] <- resultsSize[[size]] + 1
          } else {
            resultsSize[[size]] <- 1
          }
        }
      } else {
        resultsList$`No State` <- resultsList$`No State` + 1
        resultsSize$`0` <- resultsSize$`0` + 1
      }
    } else {
      resultsList$`No Run` <- resultsList$`No Run` + 1
      resultsSize$`0` <- resultsSize$`0` + 1
    }
  }
  
  paramFrame$EndStates[[i]] <- resultsList
  paramFrame$EndStatesNum[i] <- length(resultsList) - 2 # ! No State, No Run
  paramFrame$EndStateSizes[[i]] <- resultsSize
  paramFrame$EndStateSizesNum[i] <- length(resultsSize) - 1 # ! 0
  paramFrame$EndStateAssembly[[i]] <- resultsAssembly
  paramFrame$EndStateAbundance[[i]] <- resultsAbund
}
```

## Test Data

```{r addTest}
testRowNums <- nrow(paramFrame)
testRowsToAdd <- c(2, 6) # Make sure to put in numerical order!

paramFrame <- with(
  list(
    basal2 = c(5, 10, 15),
    consumer2 = c(20, 40, 60),
    logBodySize = c(-2, -1, -1, 0),
    parameters = c(0.01, 10, 0.5, 0.2, 100, 0.1)
  ),
  {
    set.seed(3680180)
    seedsPrep2 <- runif(2 * length(basal2) * length(consumer2)) * 1E8
    with(list(
      b = rep(basal2, times = length(consumer2)),
      c = rep(consumer2, each = length(basal2)),
      s1 = seedsPrep2[1:(length(basal2) * length(consumer2))],
      s2 = seedsPrep2[
        (length(basal2) * length(consumer2) + 1):(
          2 * length(basal2) * length(consumer2))
      ]
    ), {
      rbind(
        paramFrame,
        data.frame(
          CombnNum = testRowsToAdd,
          Basals = b[testRowsToAdd],
          Consumers = c[testRowsToAdd],
          SeedPool = s1[testRowsToAdd],
          SeedMat = s2[testRowsToAdd],
          SeedRuns = "",
          SeedRunsNum = 0,
          EndStates = I(rep(list(""), length(testRowsToAdd))),
          EndStatesNum = 0,
          EndStateSizes = I(rep(list(""), length(testRowsToAdd))),
          EndStateSizesNum = NA,
          EndStateAssembly = I(rep(list(""), length(testRowsToAdd))),
          EndStateAbundance = I(rep(list(""), length(testRowsToAdd))),
          Dataset = "Test",
          DatasetID = max(paramFrame$DatasetID) + 1,
          stringsAsFactors = FALSE
        )
      )
    }
    )
  }
)

testRowNums <- (testRowNums + 1):nrow(paramFrame)
resultsList <- list(
  list(
    "No Run" = 0,
    "No State" = 0,
    "2, 4, 6, 12, 29" = 1,
    "2, 4, 6, 13, 29" = 1
  ),
  list(
    "No Run" = 0,
    "No State" = 0,
    "8, 10, 12, 14, 15, 16, 39, 43" = 1,
    "8, 12, 14, 15, 16, 38, 39" = 1
  )
)
resultsSize <- list(
  list(
    "0" = 0,
    "5" = 2
  ),
  list(
    "0" = 0,
    "8" = 1,
    "7" = 1
  )
)
resultsAbund <- list(
  list(
    "No Run" = "",
    "No State" = "",
    "2, 4, 6, 12, 29" = "742.88553671712, 80.579233072626, 162.128399850253, 20.2082198699389, 18.8589490510429",
    "2, 4, 6, 13, 29" = "668.664143581837, 119.024146851052, 127.680269383867, 30.657960866033, 13.4844194707944"
  ),
  list(
    "No Run" = "",
    "No State" = "",
    "8, 10, 12, 14, 15, 16, 39, 43" = "20.7665807606491, 32.4461165261454, 80.4033387818895, 817.879722033354, 121.136570782828, 18.0390671088957, 12.3834561177271, 19.9674718543196",
    "8, 12, 14, 15, 16, 38, 39" = "82.592048492812, 138.267379166014, 938.158436379166, 51.8610963745021, 5.03556251837491, 14.1019343145825, 25.9231062711228"
  )
)

for (j in seq_along(testRowNums)) {
  i <- testRowNums[j]
  paramFrame$EndStates[[i]] <- resultsList[[j]]
  paramFrame$EndStatesNum[i] <- length(resultsList[[j]]) - 2 # ! No State, No Run
  paramFrame$EndStateSizes[[i]] <- resultsSize[[j]]
  paramFrame$EndStateSizesNum[i] <- length(resultsSize[[j]]) - 1 # ! 0
  paramFrame$EndStateAbundance[[i]] <- resultsAbund[[j]]
}

```

## Plot

```{r plot3D}
# X, Y, Basal and Consumer.
# Z = Sizes of the Endstates.

plotScalingData <- data.frame(
  CombnNum = rep(paramFrame$CombnNum, paramFrame$EndStatesNum),
  Basals = rep(paramFrame$Basals, paramFrame$EndStatesNum),
  Consumers = rep(paramFrame$Consumers, paramFrame$EndStatesNum),
  Dataset = rep(paramFrame$Dataset, paramFrame$EndStatesNum),
  DatasetID = rep(paramFrame$DatasetID, paramFrame$EndStatesNum)
)

# Communities
comms <- unlist(lapply(paramFrame$EndStates, names))
freqs <- unlist(paramFrame$EndStates)
asmbl <- unlist(paramFrame$EndStateAssembly, recursive = FALSE)
asmbl <- asmbl[comms != "No Run" & comms != "No State"]
freqs <- freqs[comms != "No Run" & comms != "No State"]
comms <- comms[comms != "No Run" & comms != "No State"]

asmbl <- lapply(asmbl, function(d) {
  if (is.null(d)) return(NA)
  if ("Result.Outcome" %in% names(d))
    d %>% dplyr::filter(Result.Outcome != "Type 1 (Failure)" & 
                          Result.Outcome != "Present")
  else
    d$Result %>% dplyr::filter(Outcome != "Type 1 (Failure)" & 
                                 Outcome != "Present")
})

plotScalingData$Communities <- comms
plotScalingData$CommunityFreq <- freqs
plotScalingData$CommunitySeq <- asmbl

# Community Size
temp <- unlist(lapply(strsplit(plotScalingData$Communities, ','), length))
plotScalingData$CommunitySize <- temp

# For usage by the reader.

plotScaling <- plotly::plot_ly(
  plotScalingData,
  x = ~Basals,
  y = ~Consumers,
  z = ~CommunitySize,
  color = ~Dataset,
  colors = c("red", "blue", "black")
)

plotScaling <- plotly::add_markers(plotScaling)

plotScaling <- plotly::layout(
  plotScaling,
  scene = list(
    xaxis = list(type = "log"),
    yaxis = list(type = "log"),
    camera = list(
      eye = list(
        x = -1.25, y = -1.25, z = .05
      )
    )
  )
)

plotScaling
```

## Abundances

```{r loadPoolsMats}
# > runif(1) * 1E8
# [1] 82598679
set.seed(82598679)

mats <- list()
poolsall <- list() # name pools used in save data; be careful!

for (i in 1:length(dirViking)) {
  temp <- load(file.path(
    dirViking[i], 
    paste0("LawMorton1996-NumericalPoolCommunityScaling-PoolMats", 
           if (i > 1) i else "", 
           ".RDS")
  ))
  mats[[i]] <- eval(parse(text = temp[1]))
  poolsall[[i]] <- eval(parse(text = temp[2]))
}
pools <- poolsall

# Add in the test datasets.
poolsTemp <- list()
matsTemp <- list()
for (r in testRowNums) {
  testRowRow <- paramFrame[r, ]
  poolsTemp[[testRowRow$CombnNum]] <- with(testRowRow,
    RMTRCode2::LawMorton1996_species(
      Basal = Basals,
      Consumer = Consumers,
      Parameters = c(0.01, 10, 0.5, 0.2, 100, 0.1),
      LogBodySize = c(-2, -1, -1, 0),
      seed = SeedPool
    )
  )
  matsTemp[[testRowRow$CombnNum]] <- with(testRowRow,
    RMTRCode2::LawMorton1996_CommunityMat(
      Pool = poolsTemp[[CombnNum]],
      Parameters = c(0.01, 10, 0.5, 0.2, 100, 0.1),
      seed = SeedMat
    )
  )
}
pools[[i + 1]] <- poolsTemp
mats[[i + 1]] <- matsTemp

oldCandidateData <- load(file.path(getwd(), "candidateDataSoFar.Rdata"))
oldCandidateData <- eval(parse(text = oldCandidateData))
```

```{r computeCandidates}
candidateData <- plotScalingData %>% dplyr::group_by(
  CombnNum, Dataset
) %>% dplyr::mutate(
  OtherSteadyStates = dplyr::n() - 1
) %>% dplyr::filter(
  OtherSteadyStates > 0
)
candidateData %>% dplyr::select(-CommunitySeq)
```

```{r loadAbundances}
# First, check if it is in the paramFrame.
# Second, check if it is in the saved data from the previous.
# Otherwise, ignore it, we'll figure out what it is and why it is missing later.

candidateData$CommunityAbund <- ""

for (r in 1:nrow(candidateData)) {
  # ID 1:4 are used to identify paramFrame, 5 used to identify abundance
  ID <- candidateData[r, 1:6]
  paramFrameRow <- paramFrame %>% dplyr::filter(
    CombnNum == ID$CombnNum,
    Basals == ID$Basals,
    Consumers == ID$Consumers,
    Dataset == ID$Dataset
  )
  
  if (is.list(paramFrameRow$EndStateAbundance[[1]])) {
    entry <- which(ID$Communities == names(paramFrameRow$EndStateAbundance[[1]]))
    if (length(entry)) {
      candidateData$CommunityAbund[r] <- paramFrameRow$EndStateAbundance[[1]][[entry]]
      next()
    }
  }
  
  if (ID$Dataset == "2021-04") {
    
    oldCandDatRow <- oldCandidateData %>% dplyr::filter(
      CombnNum == ID$CombnNum,
      Basals == ID$Basals,
      Consumers == ID$Consumers,
      Communities == ID$Communities
    )
    
    if (nrow(oldCandDatRow) > 0) {
      if (oldCandDatRow$CommunityAbund != "") {
        candidateData$CommunityAbund[r] <- oldCandDatRow$CommunityAbund
      }
    }
  }
}
```

```{r createAbund}
for (r in 1:nrow(candidateData)) {
  if (!(candidateData$CommunityAbund[r] == "Failure" |
      candidateData$CommunityAbund[r] == "")) next

  # Random guesses, starting from structured.
  temp <- with(
    candidateData[r, ],
    RMTRCode2::FindSteadyStateFromEstimate(
      Pool = pools[[DatasetID]][[CombnNum]],
      InteractionMatrix = mats[[DatasetID]][[CombnNum]],
      Community = Communities,
      Populations = ifelse(
        pools[[DatasetID]][[CombnNum]]$Type[
          RMTRCode2::CsvRowSplit(Communities)
        ] == "Basal",
        1000, 10)
    )
  )

  if (any(temp) < 1E-4) {
    temp <- "EstimateFailure"
  } else {
    temp <- toString(temp)
  }
  candidateData$CommunityAbund[r] <- temp
}
```

```{r filterNoAbund}
candidateData <- candidateData %>% dplyr::filter(CommunityAbund != "",
                                                 CommunityAbund != "Failure",
                                                 CommunityAbund != "EstimateFailure")
```

```{r computeProductivity}
candidateData$CommunityProd <- NA
for (r in 1:nrow(candidateData)) {
  candidateData$CommunityProd[r] <- with(
    candidateData[r, ], 
    RMTRCode2::Productivity(
      Pool = pools[[DatasetID]][[CombnNum]], 
      InteractionMatrix = mats[[DatasetID]][[CombnNum]], 
      Community = Communities, 
      Populations = CommunityAbund
    )
  )
}
```

## Simple Island Results
```{r islandFUN}
islandFUN <- function(i, dat, pool, mat, dmat) {
  temp <- dat[i, ]
  RMTRCode2::IslandDynamics(
    Pool = pool,
    InteractionMatrix = mat,
    Communities = c(
      list(temp$Communities[1]),
      rep("", nrow(dmat) - 2),
      temp$Communities[2]
    ),
    Populations = c(
      list(temp$CommunityAbund[1]),
      rep("", nrow(dmat) - 2),
      list(temp$CommunityAbund[2])
    ),
    DispersalPool = 0.0001,
    DispersalIsland = dmat,
    Verbose = FALSE
  )
}
```

```{r islandOneTwo}
# For each group-dataset,
# For each pair,
# Run Island Dynamics,
# Save the result with its pairing
candidateData$TotalID <- paste(candidateData$CombnNum, candidateData$DatasetID)

islandInteractionsOneTwo <- list()

for (grp in unique(candidateData$TotalID)) {
  candidateDataSubset <- candidateData %>% dplyr::filter(TotalID == grp)
  
  if (nrow(candidateDataSubset) == 1) next()
  
  pairingResults <- combn(
    nrow(candidateDataSubset), 2, 
    islandFUN,
    dat = candidateDataSubset, 
    pool = pools[[
      candidateDataSubset$DatasetID[1]
    ]][[candidateDataSubset$CombnNum[1]]],
    mat = mats[[
      candidateDataSubset$DatasetID[1]
    ]][[candidateDataSubset$CombnNum[1]]],
    dmat = matrix(c(0, 1, 1, 0), nrow = 2, ncol = 2),
    simplify = FALSE
  )
  
  pairingResults <- lapply(
    pairingResults, function(mat, isles) {
      mat <- mat[nrow(mat), -1]
      retVal <- list()
      species <- length(mat) / isles
      for (i in 1:isles) {
        retVal[[i]] <- mat[((i - 1) * species + 1) : (i * species)]
      }
      retVal
    },
    isles = 2
  )
  
  islandInteractionsOneTwo[[grp]] <- pairingResults
}
```

```{r islandOneEmptyTwo}
islandInteractionsOneEmptyTwo <- list()

for (grp in unique(candidateData$TotalID)) {
  candidateDataSubset <- candidateData %>% dplyr::filter(TotalID == grp)
  
  if (nrow(candidateDataSubset) == 1) next()
  
  pairingResults <- combn(
    nrow(candidateDataSubset), 2, 
    islandFUN,
    dat = candidateDataSubset, 
    pool = pools[[
      candidateDataSubset$DatasetID[1]
    ]][[candidateDataSubset$CombnNum[1]]],
    mat = mats[[
      candidateDataSubset$DatasetID[1]
    ]][[candidateDataSubset$CombnNum[1]]],
    dmat = matrix(c(
      0, 1, 0, # Island 2 -> 1
      1, 0, 1, # Island 1 -> 2, Island 3 -> 2
      0, 1, 0  # Island 2 -> 3
    ), nrow = 3, ncol = 3, byrow = TRUE),
    simplify = FALSE
  )
  
  pairingResults <- lapply(
    pairingResults, function(mat, isles) {
      mat <- mat[nrow(mat), -1]
      retVal <- list()
      species <- length(mat) / isles
      for (i in 1:isles) {
        retVal[[i]] <- mat[((i - 1) * species + 1) : (i * species)]
      }
      retVal
    },
    isles = 3
  )
  
  islandInteractionsOneEmptyTwo[[grp]] <- pairingResults
}
```

```{r compareIslandDynamics}
# Format of table should be:
# ID, Community 1, Community 2, Outcomes 1-2, Outcomes 1-0-2
# For outcomes, species presence will be used.

# communities <- NULL
# totalCommunities <- NULL
# for (grp in unique(candidateData$TotalID)) {
#   candidateDataSubset <- candidateData %>% dplyr::filter(TotalID == grp)
#   
#   if (nrow(candidateDataSubset) > 1) {
#     newCommunities <- combn(
#       candidateDataSubset$Communities, 2, 
#     )
#     communities <- c(communities, newCommunities)
#     totalCommunities <- c(
#       totalCommunities,
#       toString(sort(unique(unlist(lapply(newCommunities, 
#                                          RMTRCode2::CsvRowSplit)))))
#     )
#   }
# }

communities <- NULL
totalCommunities <- NULL
for (grp in unique(candidateData$TotalID)) {
  candidateDataSubset <- candidateData %>% dplyr::filter(TotalID == grp)
  
  if (nrow(candidateDataSubset) > 1) {
    newCommunities <- combn(
      candidateDataSubset$Communities, 2, 
    )
    communities <- c(communities, newCommunities)
    totalCommunities <- c( # Labelling is wrong here. Need tC from pairs of nC.
      totalCommunities,
      list(apply(newCommunities, 2, function(coms) {
        toString(sort(unique(RMTRCode2::CsvRowSplit(coms))))
      }))
    )
  }
}

# Make sure that the entries match in terms of lengths.
stopifnot(
  isTRUE(all.equal(
    lapply(islandInteractionsOneEmptyTwo, length), 
    lapply(totalCommunities, length), 
    check.names = FALSE
    )),
  isTRUE(all.equal(
    lapply(islandInteractionsOneEmptyTwo, function(d) {lapply(d, function(v) length(v[[1]]))}), 
    lapply(totalCommunities, function(d) {lapply(d, function(s) length(RMTRCode2::CsvRowSplit(s)))}), 
    check.names = FALSE
  ))
  
)

minThresh <- 1E-6
islandInteractionsOneTwoWhich <- unlist(lapply(
    seq_along(islandInteractionsOneTwo), function(i, byID, communities) {
        lapply(seq_along(byID[[i]]), function(j, system, comms) {
            lapply(system[[j]], function(pipeEnd, comms) {
                toString(RMTRCode2::CsvRowSplit(comms)[
                    which(pipeEnd > minThresh)
                ])
            }, comms = comms[[j]])
        }, system = byID[[i]], comms = communities[[i]])
    }, byID = islandInteractionsOneTwo, communities = totalCommunities
))

islandInteractionsOneEmptyTwoWhich <- unlist(lapply(
    seq_along(islandInteractionsOneEmptyTwo), function(i, byID, communities) {
        lapply(seq_along(byID[[i]]), function(j, system, comms) {
            lapply(system[[j]], function(pipeEnd, comms) {
                toString(RMTRCode2::CsvRowSplit(comms)[
                    which(pipeEnd > minThresh)
                ])
            }, comms = comms[[j]])
        }, system = byID[[i]], comms = communities[[i]])
    }, byID = islandInteractionsOneEmptyTwo, communities = totalCommunities
))

# islandInteractionsOneTwoWhich <- unlist(lapply(
#   seq_along(islandInteractionsOneTwo), function(i, x, tC) {
#     lapply(x[[i]], function(y, tC) {
#       lapply(y, function(z, tC) {
#         toString(RMTRCode2::CsvRowSplit(tC)[which(z > 1E-6)])
#       }, tC = tC)
#     },
#     tC = tC[i])
#   },
#   x = islandInteractionsOneTwo,
#   tC = totalCommunities))
# 
# islandInteractionsOneEmptyTwoWhich <- unlist(lapply(
#   seq_along(islandInteractionsOneEmptyTwo), function(i, x, tC) {
#     lapply(x[[i]], function(y, tC) {
#       lapply(y, function(z, tC) {
#         toString(RMTRCode2::CsvRowSplit(tC)[which(z > 1E-6)])
#       }, tC = tC)
#     },
#     tC = tC[i])
#   },
#   x = islandInteractionsOneEmptyTwo,
#   tC = totalCommunities))

islandInteractionResults <- data.frame(
  DatasetID = rep(names(islandInteractionsOneTwo), 
                  unlist(lapply(islandInteractionsOneTwo, length))),
  Community1 = communities[seq(from = 1, to = length(communities), by = 2)],
  Community2 = communities[seq(from = 2, to = length(communities), by = 2)],
  OutcomeWOEmpty_Island1 = islandInteractionsOneTwoWhich[
    seq(from = 1, to = length(islandInteractionsOneTwoWhich), by = 2)],
  OutcomeWOEmpty_Island2 = islandInteractionsOneTwoWhich[
    seq(from = 1, to = length(islandInteractionsOneTwoWhich), by = 2)],
  OutcomeWEmpty_Island1 = islandInteractionsOneEmptyTwoWhich[
    seq(from = 1, to = length(islandInteractionsOneEmptyTwoWhich), by = 3)],
  OutcomeWEmpty_Island2 = islandInteractionsOneEmptyTwoWhich[
    seq(from = 1, to = length(islandInteractionsOneEmptyTwoWhich), by = 3)],
  OutcomeWEmpty_Island3 = islandInteractionsOneEmptyTwoWhich[
    seq(from = 1, to = length(islandInteractionsOneEmptyTwoWhich), by = 3)]
)

islandInteractionResults
```

<!-- original outcomes before fix. 
 ```{r matches} 
 islandInteractionResults %>% dplyr::mutate( 
   C1WOInvaded = Community1 != OutcomeWOEmpty_Island1, 
   C2WOInvaded = Community2 != OutcomeWOEmpty_Island2, 
   C1WInvaded = Community1 != OutcomeWEmpty_Island1, 
   C2WInvaded = Community2 != OutcomeWEmpty_Island3, 
   StalemateWO = !C1WOInvaded & !C2WOInvaded, 
  StalemateW = !C1WInvaded & !C2WInvaded, 
  HybridWO = C1WOInvaded & C2WOInvaded, 
  HybridW = C1WInvaded & C2WInvaded, 
 ) %>% dplyr::select(-dplyr::starts_with("Outcome")) 
 ``` 
 Outcomes after fix. Note the differences in the last column for easiest comparison. -->
```{r matches2}
islandInteractionResults %>% dplyr::mutate(
  C1WOInvaded = Community1 != OutcomeWOEmpty_Island1,
  C2WOInvaded = Community2 != OutcomeWOEmpty_Island2,
  C1WInvaded = Community1 != OutcomeWEmpty_Island1,
  C2WInvaded = Community2 != OutcomeWEmpty_Island3,
  StalemateWO = !C1WOInvaded & !C2WOInvaded,
  StalemateW = !C1WInvaded & !C2WInvaded,
  HybridWO = C1WOInvaded & C2WOInvaded,
  HybridW = C1WInvaded & C2WInvaded,
) %>% dplyr::select(-dplyr::starts_with("Outcome"))
```

# Save workspace
```{r save}
save(
  candidateData,
  islandInteractionsOneEmptyTwo,
  islandInteractionsOneEmptyTwoWhich,
  islandInteractionsOneTwo,
  islandInteractionsOneTwoWhich,
  mats,
  paramFrame,
  plotScalingData,
  pools,
  file = "LM1996-NumPoolCom-QDat-2021-05.RData"
)
```
